ArticleRadiology. Imaging cancer2026
A Deep Learning Algorithm for Liver Metastasis Detection at Contrast-enhanced Abdominal CT in Patients with Colorectal Cancer: A Comparative Study with Radiologists.
Article in Radiology. Imaging cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Deep Learning-based Detection of Colorectal Liver Metastases: Performance, Robustness, and Clinical Implications.Radiology. Imaging cancer · 2026Article
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10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Purpose To evaluate the performance of a deep learning algorithm (DLA) for detecting liver metastases (LM) in patients with colorectal cancer (CRC) across diverse clinical contexts and compare its accuracy with that of radiologists. Materials and Methods This retrospective, bicentric study included patients with CRC who underwent contrast-enhanced abdominal CT between January 2019 and December 2021. The DLA accuracy was assessed at the per-nodule and per-patient levels and compared with that of a senior (R1) and an in-training (R2) radiologist blinded to each other's results. The LM detection and false detection rates and interreader agreement were determined. Results Among 181 patients with CRC (mean age, 64 years ± 13 [SD]; 102 male), 95 had LM and 86 had no LM. In the per-nodule analysis, the DLA LM detection rate was 81% (227 of 280; 95% CI: 76.1, 85.2), with no difference compared with R1 (79%; 222 of 280; 95% CI: 74.2, 83.6;
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